Detecting primitive events (for example, at a checkout) is advantageous for many applications in retail vectors (for example, loss prevention). Existing approaches include manual marking, but such approaches are expensive, time-consuming, error-prone and not scalable. Other approaches include event learning techniques that use visual information, but disadvantageously require annotation for training and may not be in real-time. Existing approaches also include using physical sensors. However, such approaches are limited to specific domains (for example, weight, height, etc.) and sensors can be expensive.